Finite Impulse Response Filter Research Articles

Trainable signal encoders that are robust against noise

Within the deep learning paradigm, finite impulse response (FIR) filters are often used to encode audio signals, yielding flexible and adaptive feature representations. We show that a stabilization of FIR filterbanks with fixed filter lengths (convolutional layers with 1-D filters) by means of their condition number leads to encoders that are optimally robust against noise and can be inverted with perfect reconstruction by their transposes. To maintain their flexibility as regular neural network layers we implement the stabilization via a computationally efficient regularizing term in the objective function of the learning problem. In this way, the encoder keeps its expressive power and is optimally stable and noise-robust throughout the whole learning procedure. We show in a denoising task where noise is additionally present in the encoder representation of the signals, that the proposed stabilization of the trainable filterbank encodings is decisive for increasing the signal-to-noise ratio of the denoised signals significantly compared to a model with a naively trained encoder.

A non-linear local active noise control with integrated virtual sensing using an adaptive hybrid spline filter

Virtual sensing technique has been a promising means for local active noise control (ANC) to surpass the design constraint of error microphone placement at a desired location. Most existing systems which use virtual sensing employ an finite impulse response (FIR) filter based adaptive filter for achieving noise cancellation. However, the effectiveness of such ANC systems deteriorate in the presence of non-linearities in the system. In this paper, an attempt has been made to develop a class of non-linear ANC systems, with integrated virtual sensing, based on adaptive spline filters. These include a Wiener model, a Hammerstein model and a sandwich Hammerstein-Wiener based hybrid model of non-linear ANC. The learning algorithms for the proposed ANC schemes have been developed and it was observed that the adaptive nature of the activation functions allow the ANC system to shift from linear to non-linear and vice versa depending on the control scenario. The simulation results demonstrate that the hybrid spline based ANC system provides an improved noise reduction performance in non-linear scenarios in comparison with other ANC schemes.

A framework for the acoustic simulation of passing vehicles using variable length delay lines

The sound produced by vehicles driving on roadways constitutes one of the dominant noise sources in urban areas. The impact of traffic noise on human activities and the related investigation on modeling, assessment, and abatement strategies fueled the research on the simulation of the sound produced by individual passing vehicles. Simulators enable in fact to promote a perceptual assessment of the nature of traffic noise and of the impact of single road agents on the overall soundscape. In this work, we present TrafficSoundSim, an open-source framework for the acoustic simulation of vehicles transiting on a road. We first discuss the generation of the sound signal produced by a vehicle, represented as a combination of road/tire interaction noise and engine noise. We then introduce a propagation model based on the use of variable length delay lines, allowing to simulate acoustic propagation and Doppler effect. The proposed simulator incorporates the effect of air absorption and ground reflection, modeled via complex-valued reflection coefficients dependent on the road surface impedance, as well as a model of the directivity of sound sources representing the passing vehicles. The source signal generation and the propagation stages are decoupled, and all effects are implemented using finite impulse response filters. Moreover, no recorded data is required to run the simulation, making the framework flexible and independent on data availability. Finally, to validate the framework capability to accurately simulate passing vehicles, a comparison between synthetic and recorded pass-by events is presented. The validation shows that sounds generated with the proposed method achieve a good match with recorded events in terms of power spectral density and psychoacoustics metrics as well as a perceptually plausible result.

A Fast iterative shrinkage and threshold algorithm-based least-squares deconvolution technique with application to bearing fault signals

Bearings are commonly subjected to load, transmission, and impact during operation, resulting in bearing failure that ultimately leads to mechanical breakdown. The low energy level of fault-induced impulsive signals, however, renders it vulnerable to being masked by environmental noise and other disturbances present in the vibration signal. To cope with this issue, this paper investigates the application of the finite impulse response (FIR) filter for bearing fault detection. In this work, the estimation of the FIR is formulated as a least squares optimization problem, aiming to minimize the l 2 norm of the FIR and effectively suppress noise and interference sources. The solutions of the least squares optimization problem are obtained using the fast iterative shrinkage–thresholding (FISTA) algorithm. Furthermore, to better select the regularization parameter in our method, an improved gray wolf optimizer (I–GWO) is employed to conduct the parameter selection. Based on these improvements, the proposed FISTA–LSD technique enables rapid and precise detection of fault characteristics upon their occurrence, thereby mitigating the propagation of faults and preventing accidents from happening. Furthermore, with this automated selection process, engineers can save valuable time that would otherwise be spent on trial-and-error approaches. Through its rapid response capabilities and precise fault detection abilities enabled by the FISTA–LSD technique along with I–GWO integration for automatic parameter selection; this FISTA–LSD technique greatly enhances its practical value. The proposed technique is validated through the utilization of both numerical and experimental data.

Dynamic decoupling and compensation based on the inverted network technique and experiment research for multi-axis hydraulic road simulator

This paper presents a dynamic decoupling and compensation approach to eliminate channel interaction for a multi-axis hydraulic road simulator. Misalignment of the concentrated moving mass centroid on the entire test bench with the control point and inconsistencies in actuator dynamics leads to dynamic coupling, which deteriorates the motion performance of the system. To solve the problem, an inverted decoupling and compensation network based on an identification model is proposed. Firstly, the system is modeled using the recursive extended least square (RELS) identification method. In the feedforward compensation modeling process, the steady-state inverse is obtained using zero-magnitude error technology control (ZMETC). In addition, a finite impulse response (FIR) filter with an adaptive algorithm is employed to reduce the adverse impact of the modeling error. The proposed decoupling strategy is validated by conducting experiments using a multi-axis hydraulic road simulator. The experimental results indicate that the designed compensator can effectively decrease the cross-coupling of multi-input and multi-output (MIMO) systems. The suggested approach is also applicable to applications that necessitate the elimination of interactions between different control variables.

Evolution characteristics of an induced electric charge signal and identification method of charge for instability failure in loaded coal

A coal-loaded charge induction monitoring system is developed to effectively forecast the dynamic disasters caused by coal failure. Specifically, a digital finite impulse response (FIR) filter is designed to denoise and filter the signal, and the time-frequency domain evolution of induced charge signals is analyzed during coal failure experiments. The quantitative relationships between the induced electric charge and stress-strain energy, and ultimately, between induced electric charge and coal deformation/failure, are revealed. Ultimately, the electric charge sensor exhibits high signal collection frequency and high sensitivity, and the FIR low-pass filter constructed in MATLAB effectively denoises and filters induced charge signals. The main frequency range of the white noise is 50–500 Hz, and the main frequency of the charge signal induced by coal deformation and failure is concentrated in the range of 0–50 Hz. The optimal distances for monitoring cubic and cylindrical raw coal samples using this sensor are 9 mm and 11 mm, respectively. Notably, strain energy is released faster when it can dissipate more readily, and induced charge pulses become denser when more intense signals produce large fluctuations. A method is proposed to identify coal deformation and failure based on changes in the induced electric charge. This study provides a new means of monitoring the early warning signs of dynamic coal mine disasters. Based on our experimental results and conclusions, a new method is proposed to identify coal deformation and failure based on changes in the induced electric charge. The precursor to the moment of coal failure can be identified by monitoring the amplitude of the induced charge, the dynamic trend of fluctuation, and the cumulative number of induced electric charge pulses during the process of coal deformation.

Higher order and optimized symmetric 2D FIR filter design and hardware architecture implementation using CSD-CSE

In this paper, an optimized and high-performance Two-Dimensional (2D) Finite Impulse Response (FIR) filter is designed and hardware architecture is implemented for image processing applications. The higher-order circular symmetric 2D FIR filter is designed using a modified McClellan Transformation called a P4 transformation. The perfect circular symmetry in the contour of the 2D FIR filter and less complexity and error are attained by this proposed P4 Transformation. Next, the designed filter coefficients are represented by the Canonical Signed Digit (CSD) number format to attain the multiplier-less design to avoid the power complexity multipliers. Further, to reduce the hardware complexity, the Common Subexpression Elimination (CSE) technique is utilized to reduce the number of adders and hence area and power are decreased. Each sub-filter corresponding to the CSD coefficient rows is realized and integrated as a 2D FIR filter using a Fully Direct-type architecture. This 2D FIR filter architecture is coded by Verilog and synthesized by Cadence tools in a 45 nm CMOS technology library. The Delay, Power Consumption (PC), and Area reports were generated by the Genus synthesis tool and compared with the state-of-the-art works. The PC, area, and delay of the proposed filter architecture are reduced to the minimum of 13.96%, 69.1%, and 1.4%, respectively when compared to the existing filter architectures. The maximum of 10.48 times and a minimum of 1.18 times of Area Delay Product (ADP), and a maximum of 10.69 times and a minimum of 1.063 times of Power Delay Product (PDP) are smaller than the existing 2D filter architectures.

Design, hydrodynamics analysis, and control of an underwater glider with controllable wing mechanism

Due to the effect of biofouling and different mission sensors that may be onboard, hydrodynamic coefficients of underwater glider (UG) may experience gradual or obvious changes, which increases the energy consumption and reduces the glide range. This paper proposes a controllable wing mechanism (CWM) for UG, which enables it adjust the hydrodynamic coefficients and kinematic performance without changing the net buoyancy. Only by two degrees of freedom (DoF), that CWM can realize the control of wingspan, sweep back angle and chord width. To apply the hydrodynamics controlled with CWM in glide model, the fitting precision of hydrodynamics function is improved based on backpropagation artificial neural network (BPANN). A nonlinear state observer based on finite impulse response (FIR) filter is employed for state estimation and random noise interference filtering to realize measurements and evaluation of velocity and reduce the attitude angle measurement error for UG. Then, the FIR-based fuzzy PID (FFPID) controller is established for a steady-state gliding motion control, which suppresses the unknown interference to stablize the system quickly. Finally, the effectiveness of the FFPID closed-loop controller and observer is verified through the simulation in matlab and the experimental test of the UG with CWMs in a water tank.

Prototype Implementation of a Digitizer for Earthquake Monitoring System.

A digitizer is considered one of the fundamental components of an earthquake monitoring system. In this paper, we design and implement a high accuracy seismic digitizer. The implemented digitizer consists of several blocks, i.e., the analog-to-digital converter (ADC), GPS receiver, and microprocessor. Three finite impulse response (FIR) filters are used to decimate the sampling rate of the input seismic data according to user needs. A graphical user interface (GUI) has been designed for enabling the user to monitor the seismic waveform in real time, and process and adjust the parameters of the acquisition unit. The system casing is designed to resist harsh conditions of the environment. The prototype can represent the three component sensors data in the standard MiniSEED format. The digitizer stream seismic data from the remote station to the main center is based on TCP/IP connection. This protocol ensures data transmission without any losses as long as the data still exist in the ring buffer. The prototype was calibrated by real field testing. The prototype digitizer is integrated with the Egyptian National Seismic Network (ENSN), where a commercial instrument is already installed. Case studies shows that, for the same event, the prototype station improves the solution of the ENSN by giving accurate timing and seismic event parameters. Field test results shows that the event arrival time and the amplitude are approximately the same between the prototype digitizer and the calibrated digitizer. Furthermore, the frequency contents are similar between the two digitizers. Therefore, the prototype digitizer captures the main seismic parameters accurately, irrespective of noise existence.

Understanding How Filters Affect Data

There are many filters in digital data acquisition and analysis. These filters have both positive and negative effects on the data. While the positive effects (like removing signal in specified frequency bands) are obvious. The negative effects (like signal delays) may not be so obvious. This presentation will discuss in detail the negative effects on the data and how these effects can be mitigated. We will cover the Finite Impulse Response (FIR) and Infinite Impulse Response (IIR) filters.

Adaptive mode decomposition method based on fault feature orientation and its application to compound fault diagnosis of planetary gearboxes

Abstract Planetary gearboxes often experience multiple component failures during service, which can accelerate the degradation and failure of industrial equipment. Accurate separation and identification of multiple faults is an important means of ensuring the safe and stable operation of equipment. However, different faults can interact with each other, along with the influence of background noise, making it challenging to accurately extract faults with relatively weak energy among multiple faults. This difficulty leads to the problems of potential misdiagnosis and underdiagnosis. To address this issue, an adaptive mode decomposition method based on fault feature orientation (AMD-FF) is proposed in this paper. Initially, a fault impact indicator (FII) is constructed based on period-weighted kurtosis of envelope spectral and correlated combination negentropy to effectively characterize the impulsiveness and periodicity of fault features. Furthermore, with the objective of maximizing the FII, an adaptive decomposition of the original signal is designed based on blind convolution theory using a finite-impulse response filter group. Subsequently, a variable weight particle swarm optimization is employed to adaptively optimize the key decomposition parameters. Finally, the data of industrial-grade planetary gear transmission test rig are collected to validate the proposed method for compound fault diagnosis of planetary gearboxes. The results indicate that the AFMD-FF can effectively separate and extract compound faults in planetary gearboxes, demonstrating superior fault separation and diagnostic performance compared to the fault mode decomposition (FMD) and adaptive FMD. This method offers a novel approach to diagnosing compound faults in rotating equipment in industrial scenarios.

Certain investigation on hybrid neural network method for classification of ECG signal with the suitable a FIR filter

The Electrocardiogram (ECG) records are crucial for predicting heart diseases and evaluating patient’s health conditions. ECG signals provide essential peak values that reflect reliable health information. Analyzing ECG signals is a fundamental technique for computerized prediction with advancements in Very Large-Scale Integration (VLSI) technology and significantly impacts in biomedical signal processing. VLSI advancements focus on high-speed circuit functionality while minimizing power consumption and area occupancy. In ECG signal denoising, digital filters like Infinite Impulse Response (IIR) and Finite Impulse Response (FIR) are commonly used. The FIR filters are preferred for their higher-order performance and stability over IIR filters, especially in real-time applications. The Modified FIR (MFIR) blocks were reconstructed using the optimized adder-multiplier block for better noise reduction performance. The MIT-BIT database is used as reference where the noises are filtered by the MFIR based on Optimized Kogge Stone Adder (OKSA). Features are extracted and analyzed using Discrete wavelet transform (DWT) and Cross Correlation (CC). At this modern era, Hybrid methods of Machine Learning (HMLM) methods are preferred because of their combined performance which is better than non-fused methods. The accuracy of the Hybrid Neural Network (HNN) model reached 92.3%, surpassing other models such as Generalized Sequential Neural Networks (GSNN), Artificial Neural Networks (ANN), Support Vector Machine with linear kernel (SVM linear), and Support Vector Machine with Radial Basis Function kernel (SVM RBF) by margins of 3.3%, 5.3%, 23.3%, and 24.3%, respectively. While the precision of the HNN is 91.1%, it was slightly lower than GSNN and ANN but higher than both SVM linear and SVM -RBF. The HNN with various features are incorporated to improve the ECG classification. The accuracy of the HNN is switched to 95.99% when the DWT and CC are combined. Also, it improvises other parameters such as precision 93.88%, recall is 0.94, F1 score is 0.88, Kappa is 0.89, kurtosis is 1.54, skewness is 1.52 and error rate 0.076. These parameters are higher than recently developed models whose algorithms and methods accuracy is more than 90%.

Digital Aided Analog Data Acquisition for Telemetry

This paper tackles the limitations in the current data acquisition system through the integration of a equiripple Finite Impulse Response (FIR) filter. The inclusion of this digital filter eliminates the necessity for physical component alterations, thereby increasing the system’s adaptability and versatility in telemetry applications. The FIR filter’s ability to adjust passband, stopband, and sampling frequencies allows for seamless reconfiguration without the need for hardware modifications, representing a substantial enhancement compared to the constraints of the previous system. Additionally, the equiripple design guarantees minimal variations in passband and stopband ripples, thereby optimizing overall filter performance. An important aspect of the proposed solution is the implementation of shift-and-add multiplier architecture for the FIR filter, which contributes to a reduction in hardware complexity and resource requirements, ultimately leading to a more efficient system. The adoption of MATLAB’s robust filter design tools, coupled with subsequent verification through SIMULINK simulations, has played a pivotal role in successfully realizing the proposed design. To sum up, this project has efficiently addressed the identified issues by introducing a reconfigurable FIR filter solution that not only enhances the flexibility, performance, and user friendliness of the data acquisition system in telemetry applications but also showcases the potential for broader applications in various signal processing domain.

Digital cancellation of multi-band passive inter-modulation based on Wiener-Hammerstein model

Utilizing multi-band and multi-carrier techniques enhances throughput and capacity in Long-Term Evolution (LTE)-Advanced and 5G New Radio (NR) mobile networks. However, these techniques introduce Passive Inter-Modulation (PIM) interference in Frequency-Division Duplexing (FDD) systems. In this paper, a novel multi-band Wiener-Hammerstein model is presented to digitally reconstruct PIM interference signals, thereby achieving effective PIM Cancellation (PIMC) in multi-band scenarios. In the model, transmitted signals are independently processed to simulate Inter-Modulation Distortions (IMDs) and Cross-Modulation Distortions (CMDs). Furthermore, the Finite Impulse Response (FIR) filter, basis function generation, and B-spline function are applied for precise PIM product estimation and generation in multi-band scenarios. Simulations involving 4 carrier components from diverse NR frequency bands at varying transmitting powers validate the feasibility of the model for multi-band PIMC, achieving up to 19 dB in PIMC performance. Compared to other models, this approach offers superior PIMC performance, exceeding them by more than 5 dB in high transmitting power scenarios. Additionally, its lower sampling rate requirement reduces the hardware complexity associated with implementing multi-band PIMC.

Convex Optimization Based Design of FIR Filters for Reference Shaping

Abstract This paper explores design of Finite Impulse Response filters for controlling underdamped systems while dealing with uncertainties in model parameters. By setting magnitude constraints in the frequency domain within a convex programming framework, it ensures that dominant resonant modes are attenuated at the end of the maneuver, high-frequency unmodeled modes are not excited, and there is no inordinate accentuation of frequencies in the passband of the filter. A mobile platform with an attached flexible beam serves as a testbed to validate the designs for rest to rest maneuvers, demonstrating how different cost functions of error between the desired and optimized magnitude response affect the filter performance. The study also examines robustness in the notch area by shifting the natural frequencies of the system by shifting a tip mass at the free end of the beam. The total energy at the final maneuver time of the first three system modes is calculated as a vibration suppression metric and is used to compare established input shapers with the proposed Finite Impulse Response filters.

Fine-tuning digital FIR filters with gray wolf optimization for peak performance

The design of optimum filters constitutes a fundamental aspect within the realm of signal processing applications. The process entails the calculation of ideal coefficients for a filter in order to get a passband with a flat response and an unlimited level of attenuation in the stopband. The objective of this work is to solve the FIR filter design problem and to compare the optimal solutions obtained from evolutionary algorithms. The design of optimal FIR low pass (LP), high pass (HP), and band stop (BS) filters is achieved by the utilization of nature-inspired optimization approaches, namely gray wolf optimization ,cuckoo search, particle swarm optimization, and genetic algorithm. The filters are evaluated in terms of their stop band attenuation, pass band ripples, and departure from the anticipated response. In addition, this study compares the optimization strategies applied in the context of algorithm execution time which is achievement of global optimal outcomes for the design of digital finite impulse response (FIR) filters. The results indicate that when the Gray wolf algorithm is applied to the development of a finite impulse response (FIR) filter, it produces a higher level of performance than other approaches, as supported by enhanced design precision, decreased execution time, and achievement of an optimal solution.

High Performance CNN Accelerators based on Hardware and Algorithm Co-Optimization

The efficacy of convolutional neural networks (CNNs) has led to their extensive application in picture recognition and categorization. Nevertheless, embedded systems face challenges when it comes to storing the massive volumes of weight data required by CNNs in their on-chip memory. Pruning a convolutional neural network (CNN) model can reduce its size with no impact on accuracy; however, when used with a parallel architecture, the resulting model will be slower to execute. A CNN compression method that is hardware-centric is introduced in this paper. A model of a deep neural network (DNN) will include "pruning layers (P-layers)" and "no-pruning layers (NP-layers)" attached to it. For effective and parallel processing, an NP-layer employs a regular distribution for its weights. Pruning causes a P-layer to have an irregular form, yet the high compression ratio it yields makes the shape undesirable. Using uniform and gradual quantization methods, we can achieve a processing efficiency/compression ratio trade-off with a small loss of precision. Integrating multiple parallel finite impulse response (FIR) filters into an NP-layer distributed convolutional architecture, we further improve the regular model. The P-layer irregular sparse model is suggested to use an ADF-based processing element. The suggested compression strategy and hardware architecture can be utilised by a hardware/algorithm co-optimization (HACO) method to run an NP→P hybrid compressed CNN model on FPGAs. The VGG-16 network achieves a compression ratio of 27.5× with a top-5 accuracy loss of 0.44% by using a hardware accelerator on a single FPGA chip without off-chip memory. A result of 83.0 FPS, or 1.8 times quicker, was achieved when the compressed VGG-16 model was implemented on a Xilinx VCU118 evaluation board for image applications. Index Terms - CNN, Accelerators, Co-Optimization.

Area-Delay-Power Efficient VLSI Architecture 2D FIR Filter using Modified Multipliers and Adders

In this paper, a low power, area, and delay 2D Finite Impulse Response (FIR) filter architecture is derived from an analysis of a memory-efficient design. The completely direct-form 2D FIR filter is where the idea of parallel processing is first presented. As a result, the FIR filter may make better use of its memory by reusing its contents. With a block size of L and a filter length of N, a non-separable 2D FIR filter structure is developed and implemented. The FIR filter's arithmetic module makes use of high-speed, low-power multipliers and Carry Look Ahead (CLA) adders, with the output calculated by a pipelined adder unit. Verilog HDL code is used to represent the proposed architecture, and the CADENCE environment's NC Simulator and RTL Compiler synthesis tool are used to verify the design. Existing memory-efficient 2D FIR filter hardware architectures are compared to the produced area, power, and delay reports. Using Modified CLA (MCLA) adders and pipelining, we were able to cut down on power consumption by 44% and delay by 20%. Index Terms: Memory reuse, 2D-FIR filter, Low Power Multiplier, Parallel Prefix Adder, Carry Look Ahead adder, and pipelining.

Design and Implementation of Digital Down Converter for WiFi Network

This paper introduces a field-programmable gate array (FPGA)-based digital down converter (DDC) processing a sampling frequency of about 3.64 GHz to a down-converted frequency of 28.4375 MHz to match the IEEE 802.11ah Wi-Fi HaLow standard. The proposed DDC uses a polyphase mixer(PM) and a resampling filter. The PM adopts parallel coordinate rotation digital computer (CORDIC) processors and lowpass filter arrays to reduce high-speed data rates with minimum resource utilization. Again, the resampling filter employs a cascaded integrator comb (CIC) filter associated with a parallel prefixed adder (PPA) and a multi-channel systolic finite impulse response (FIR) filter implemented with canonical expression, attaining optimum hardware cost. Converting floating-point to fixed-point data types provides significant resource savings. Finally, the improved design is coded in the Xilinx Vivado synthesis tool and successfully tested on the FPGA Kintex-7 device. In contrast to other recent architectures, the proposed design substantially reduces area requirements and power utilization. The Matlab tool verifies the design to achieve an acceptable spurious-free dynamic range (SFDR) of 115 dB.

An Integrated Approach towards VLSI Implementation of SFQ Logic using Standard Cell Library and Commercial Tool Suite

Abstract The semiconductor industry seeks energy-efficient alternatives as Moore’s law nears its limits. The Single Flux Quantum (SFQ) integrated circuits (ICs) using thousands of niobium Josephson junctions (JJs) and operating at 4 K show great promise for digital computing circuits at high speed (>20 GHz) and low power (a few nW per junction). The leading logic families are Rapid Single Flux Quantum (RSFQ), and its energy-efficient variant (ERSFQ). IARPA’s SuperTools program aims to develop integrated design tools for superconductor electronics, targeting SFQ and Adiabatic Quantum-Flux-Parametron (AQFP) logic families. This paper presents a passive transmission line (PTL) based standard cell library for SFQ logic, designed with Synopsys Electronic Design Automation (EDA) software tools for MIT-LL 100μA/μm2 SFQ5ee fab node. The dual RSFQ/ERSFQ standard cell library facilitates seamless integration of SFQ RTL-to-GDS design flow with Synopsys Fusion Compiler, an automated design tool. The SFQ RTL-to-GDS flow entails logic synthesis, checking, placement, clock synthesis, and routing. Row-based placement for library cells and H-tree clock tree structures are employed. Fusion Compiler’s effectiveness is validated with Hypres designs such as finite impulse response (FIR) filters, scalable multiply-accumulate (MAC) units, and memory arrays, comparing single and dual clocking schemes. The synergy between Hypres and Synopsys achieves a milestone by demonstrating the design of a digital superconducting circuit with over 10 million JJs, facilitated by a fully automated design tool for the first time. Challenges in very large-scale SFQ scaling are also discussed.